Received signal strength indicator snapshot analysis

ABSTRACT

The disclosed subject matter provides for received signal strength indicator (RSSI) snapshot analysis. RSSI snapshot analysis can be independent of determining location/map information. An RSSI snapshot can be analyzed in view of historic RSSI information to determine a probability that a local wireless resource correlated with the historical RSSI information is instantly available. Machine learning can be employed to train an inference component to facilitate in determining the probability. In an aspect, the state of a wireless radio can be controlled based on the probability and this can reduce the energy consumption of the user equipment by facilitating intelligent enablement of a wireless radio.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.12/883,145, “Wi-Fi Intelligent Selection Engine”, filed Sep. 15, 2010,which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosed subject matter relates to analysis of radio frequency (RF)resources and, more particularly, to the analysis of the RF resources inview of historical RF information to determine a probability of a localwireless resource being available.

BACKGROUND

Mobile devices such as cellular telephones, PDAs, etc. are proliferatinglike never before. Almost everyone has some sort of mobile device, andsome people have multiple devices. Users can access several differentnetworks using a single mobile device, and can access voice, text, andmultimedia data from other network entities such as servers and othermobile devices. Further, mobile device complexity is increasing, withmore and more advanced and power efficient processors, displayinterfaces, and applications to provide a user experience like neverbefore. Such devices include, for instance, the iPhone, iPad, Droid, andother PDAs/netbooks. Consequently, users are using their mobile devicesmore frequently, and have larger bandwidth requirements for data, email,voice, etc.

This increased usage puts an increased strain on the wireless networksthat provides these services. Even with the advent of 3G and 4G networksthat use Internet Protocol (IP) addressing, Session Initiation Protocol(SIP), etc., there are certain network elements that get overwhelmed andcan create a bottleneck for data flow, such as cellular base stations(or NodeBs) and their associated gateways. Several users within therange of one or more base stations who are downloading high-volume datafrom the network will have greater transmission power requirements fromthe base station. This may cause reduced signal strength per mobiledevice, and consequently a lower quality connection. Transmission powercontrol can alleviate some but not all of these issues. This furthercauses higher battery usage on the mobile device itself.

Network operators generally offer alternative means to connect to theircore networks, or to the Internet. Femtocells, Fiber-to-the-node, andwireless local area network (WLAN or Wi-Fi) access points can provideaccess to various networks for mobile devices having more than one typeof transceiver. For instance, the iPhone includes a Wi-Fi transceiver. AWi-Fi hotspot access point can be used to connect to a network, withbroadband speeds, and the load on the cellular network can be reduced.However, there are specific issues that prevent the efficient selectionof an access point. For instance, many users appear to disable Wi-Fi,for example, due to concerns over battery life. Consequently, usersoften do not enable Wi-Fi as they may forget to turn it off afterwards.Leaving Wi-Fi on can lead to faster battery drainage, while leaving itoff can lead to connectivity issues as well as sub-optimal power usageas the cellular transceiver may have to use more power forhigh-throughput communication with a base station.

Conventional techniques for determining the availability of localwireless resources, e.g., Wi-Fi hotspots, etc., typically rely onscanning for those resources or accessing location-centric data maps ofthose resources. Scanning for local wireless resources can be energyintensive and can be associated with shortened periods of time betweenrecharging cycles in battery operated user equipment (UE). Scanninggenerally is associated with a radio, adapted to operate at thefrequencies of the local wireless resource, being in an ‘on’ or ‘active’state. The active radio then listens for handshake signals from anyavailable local wireless resources at the related frequencies. Where ahandshake signal is detected, the UE can then follow predeterminedprocedures related to detecting the handshake, such as forming acommunicative link with the detected local wireless resource. As areadily appreciated example, a smartphone can have a Wi-Fi radio left onsuch that, as the smartphone enters a detected region of Wi-Fi coverage,the smartphone can attempt to log into the Wi-Fi resource. It will alsobe appreciated that leaving the exemplary smartphone Wi-Fi radio ontypically results in an increase in battery drain.

Location-centric techniques that access maps of local resources can beemployed to selectively activate (or deactivate) energy intensivecomponents based on the location of a UE. This can decrease the rate atwhich a UE battery would be discharged in comparison to leaving a radioon in a ‘scanning’ mode. As an example, a GPS-enabled phone can access amap of Wi-Fi hotspot locations. Where the phone is determined to be at aparticular location correlating to a location on the map associated witha Wi-Fi resource, the phone can activate a Wi-Fi radio. Similarly, wherethe phone is determined to be at a particular location correlating to alocation on the map not associated with a Wi-Fi resource, the phone candeactivate the Wi-Fi radio to conserve energy and prolong battery usage.While improving battery usage, location-centric techniques foreseeablyrely on maps of local wireless resources and a location determinationcomponent to correlate the UE position to a location on the map. Assuch, map data would be compiled and then accessed by a UE to determineif a local wireless resource is available. Moreover, determining alocation can require computational resources. Further, some UEs may notbe equipped to determine a location for a location-centric determinationof the availability of local wireless resources.

The above-described deficiencies of conventional mobile device locationdata sources for transportation analytics is merely intended to providean overview of some of problems of current technology, and are notintended to be exhaustive. Other problems with the state of the art, andcorresponding benefits of some of the various non-limiting embodimentsdescribed herein, may become further apparent upon review of thefollowing detailed description.

SUMMARY

The following presents a simplified summary of the disclosed subjectmatter in order to provide a basic understanding of some aspects of thevarious embodiments. This summary is not an extensive overview of thevarious embodiments. It is intended neither to identify key or criticalelements of the various embodiments nor to delineate the scope of thevarious embodiments. Its sole purpose is to present some concepts of thedisclosure in a streamlined form as a prelude to the more detaileddescription that is presented later.

Various embodiments relate to analysis that determines a probabilitythat a local wireless resource is available based on one or morereceived signal strength indicator and historical received signalstrength indicator information. In an embodiment, a system can comprisea capture component to receive signal strength indicator informationthat can be associated with user equipment such as a mobile device, forinstance a mobile phone. The exemplary system can further comprise ahistorical information component to receive historical signal strengthindicator information. An analysis component of the exemplary system candetermine a probability based on an analysis of the signal strengthindicator information and historical signal strength indicatorinformation.

In a further embodiment, a method comprises receiving signal strengthindicator information associated with a user equipment and receivinghistorical signal strength indicator information. The example methodfurther comprises determining a probability based on the signal strengthindicator information and the historical signal strength indicatorinformation.

In another example embodiment, a computer-readable storage mediumcomprises instructions for receiving signal strength indicatorinformation associated with a user equipment and receiving historicalsignal strength indicator information. Receiving historical signalstrength indicator information can include receiving informationrelating to a correlated historically available local wireless resource.The computer-readable storage medium further comprises instructions fordetermining a probability based on the signal strength indicatorinformation and the historical signal strength indicator information.The probability can be related to the present availability of thecorrelated historically available local wireless resource.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages, and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the drawings. It will also be appreciatedthat the detailed description may include additional or alternativeembodiments beyond those described in this summary.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of a system that facilitates RSSI snapshotanalysis in accordance with aspects of the subject disclosure.

FIG. 2 is a depiction of a system that facilitates RSSI snapshotanalysis in accordance with aspects of the subject disclosure.

FIG. 3 illustrates a system that facilitates RSSI snapshot analysisemploying an inference in accordance with aspects of the subjectdisclosure.

FIG. 4 illustrates an exemplary system including RSSI snapshot analysisin accordance with aspects of the subject disclosure.

FIG. 5 illustrates an exemplary system including inference based RSSIsnapshot analysis in accordance with aspects of the subject disclosure.

FIG. 6 illustrates a method facilitating RSSI snapshot analysis inaccordance with aspects of the subject disclosure.

FIG. 7 illustrates a method for changing the state of a wireless radiobased on RSSI snapshot analysis in accordance with aspects of thesubject disclosure.

FIG. 8 illustrates a method facilitating RSSI snapshot analysis based oninference in accordance with aspects of the subject disclosure.

FIG. 9 is a block diagram of an exemplary embodiment of a mobile networkplatform to implement and exploit various features or aspects of thesubject disclosure.

FIG. 10 illustrates a block diagram of a computing system operable toexecute the disclosed systems and methods in accordance with anembodiment.

DETAILED DESCRIPTION

The presently disclosed subject matter illustrates received signalstrength indicator (RSSI) snapshot analysis. RSSI is a radio receivermetric that relates to the relative received signal strength in awireless environment, an indication of the power level being received byan antenna. As such, the higher the RSSI value or less negative the RSSIvalue, the stronger the signal. A RSSI value can be associated with awireless radio signal for a wireless network carrier resource, forinstance, a RSSI value can be associated with a signal received from acell tower.

RSSI values can be readily affected by geometric and environmentalfactors. Geometric factors related to the attenuation of a radio signalas a function of distance from the source of the radio signal. As such,a RSSI value will generally fall off as the distance between thetransmitter and receiver increases. As an example, the further acellular phone is from a cell tower, the lower the RSSI value associatedwith that cell tower with typically be.

Environmental factors can also significantly affect a RSSI value.Environmental factors can include, weather, humidity, pollution levels,electromagnetic (EM) interference, trees, buildings, vehicles, UEantenna orientation, etc. As such, RSSI values generally change overtime as an environment evolves. Given that real world environments areoften constantly undergoing change, RSSI values would also experiencefluctuation.

In a truly static environment, a RSSI value might be expected to besimilarly static. In a truly static environment, a RSSI value would thenbe the same where the receiver, e.g., a UE, is in the same geometricorientation. Thus, for a UE, a ‘snapshot’ of RSSI values can be capturedfor a plurality of signal sources that would reflect only the geometricorientation of the UE where the environment is static. Data indicatingthe presence of a local wireless resource can be correlated to sets ofRSSI values. Where the snapshot matches a set of RSSI values correlatedto a local wireless resource, a static RSSI snapshot analysis, apresumption can be made that the wireless resource is available. Thispresumption can be similar to asserting that a wireless resource islocated at a particular location but is different from that assertion inthat a location is never actually determined when employing a staticRSSI snapshot analysis. This static RSSI snapshot analysis is only validwhere the RSSI is variable only as a function of geometric orientation.Where environmental factors are considered, the RSSI snapshot is likelyto differ from historical RSSI data for the same geometric orientation.

Analysis of an RSSI snapshot, the RSSI snapshot including informationfor one or more RSSIs, can still be useful by applying a probability tothe likelihood of a local wireless resource being available for a RSSIsnapshot compared to historical RSSI information. As an example, for acellphone used at a worker's desk each day, the RSSIs at the desk fromseveral cell towers may vary only by a few dB. The RSSI snapshot eachday can be correlated with the presence of an office Wi-Fi resource. ARSSI snapshot can be captures and compared to the historical RSSIinformation, and while a perfect match may not be found, it is possiblethat the RSSI snapshot can still be unique enough to allow for adetermination that it is probable the office Wi-Fi is available.Similarly, where the same cellphone captures a RSSI snapshot in thelobby of the building, the RSSI snapshot can be sufficiently differentfrom that historically associated with the office Wi-Fi resource, e.g.,the snapshots associated with the desk location, that a low probabilitycan be determined that the office Wi-Fi is available. Determining aprobability of a local wireless resource being available can be employedby a UE to change the state of a wireless radio, such as turning a radioon or off based on the determined probability. Continuing the previousexample, where the RSSI snapshot in the lobby of the office has a lowprobability, the cellphone Wi-Fi radio can be kept off, but when thecellphone arrives at the desk and an RSSI snapshot is employed indetermining a high probability of the office Wi-Fi being available, thecellphone Wi-Fi radio can be turned on to facilitate logging onto theoffice Wi-Fi resource.

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

FIG. 1 is an illustration of a system 100, which facilitates RSSIsnapshot analysis in accordance with aspects of the subject disclosure.System 100 can include RSSI information capture component (RSSI-ICC)110. RSSI-ICC 110 can facilitate capturing RSSI information. In anaspect, this RSSI information can be associated with a set of RFsignals. The set of RF signals can include RSSI information for one ormore RF signals or can be for an empty set of no RF signals. The RSSIinformation can be interchangeably referred to as a ‘snapshot’ or ‘RSSIsnapshot’ for a set of RF signals at a particular point in time. As anexample, RSSI-ICC 110 can receive RSSIs for five cell towers and theseRSSIs can comprise a snapshot of the RSSI information for the set offive cell towers at a first time, t=0. A second snapshot can be for thesame five cell towers at a second time, t=1. A third snapshot can be forthree of the five cell towers at t=0. A fourth exemplary snapshot can beof five different cell towers at t=0. Numerous other examples of asnapshot are to be appreciated as within the scope of the presentlydisclosed subject matter despite not being explicitly recited forclarity and brevity. It will be noted that a RSSI snapshot is notnecessarily associated with any particular location and rather can besimply a collection of RF signal information at a particular time. Thesnapshot can theoretically match a plurality of location wherein thoselocations each exhibit highly similar RF signal information althoughthis is highly unlikely. By employing a RSSI snapshot that isindependent of an ascertained location, the RSSI snapshot can beanalyzed without needing to be correlated to map-type information orlocation-type information. As an example, a snapshot can be directlycompared with historical RSSI information without first converting it tolocation information as is more conventionally done. An exemplaryRSSI-ICC 110 can be a wireless radio in a mobile phone, wherein thewireless radio can receive a wireless radio signal and can determine arelative signal strength for that wireless radio signal. It is to benoted that numerous other examples of RSSI-ICCs can be recited but arenot herein for the sake of clarity and brevity.

System 100 can also include RSSI historical information component(RSSI-HIC) 120. RSSI-HIC 120 can allow system 100 to receive historicalRSSI information. Historical RSSI information can be any type of RSSIinformation pertinent to an analysis of one or more snapshots fromRSSI-ICC 110. Historical RSSI information can include identifiersassociated with a signal source, dates, times, associated environmentalconditions, RSSI historic values, RSSI historic averages, RSSI historicdeviation, etc. Historical RSSI information can also include informationcorrelating one or more local wireless resources with RSSI information.This correlating information can include an identifier for a localwireless resource, a status for a local wireless resource, an accessprotocol for a local wireless resource, one or more service metrics fora local wireless resource such as min/max throughput, average bandwidth,quality of service (QoS), level of service (LoS), etc., cost metrics fora local wireless resource, etc. In an embodiment, RSSI-HIC 120 canreceive historical RSSI information that can facilitate a directcomparison with one or more RSSI snapshots. In another embodiment,RSSI-HIC 120 can receive historical RSSI information that can beemployed in determining an inference that can be employed in an analysisrelated to a RSSI snapshot. An exemplary RSSI-HIC 120 can be a datastore in a mobile device that is at least partially porpulated withhistorical RSSI information. A second exemplary RSSI-HIC 120 can be adata store located at a radio access network (RAN) component, forinstance a NodeB, that can source historic RSSI information to othercomponents of a radio access network associated with the RAN. It will benoted that numerous other examples of RSSI-HICs can be recited but arenot explicitly recited here for the sake of clarity and brevity.

RSSI-ICC 110 and RSSI-HIC 120 can be communicatively coupled to snapshotanalysis component 130. Snapshot analysis component 130 can analyze aRSSI snapshot to determine a probability that a local wireless resourceis available. This analysis can employ historical RSSI information, suchas that received by RSSI-HIC 120. In an embodiment, an analysis cancompare a RSSI snapshot to historical RSSI information to find a match.Where a match is found with an RSSI snapshot, it can be determined thatlocal wireless resources related to the historical RSSI information canbe available. This determination can be made accessible, such as, to aUE. Where local wireless resources are determined to be available, theexemplary UE can switch an appropriate radio on to attempt to access theavailable wireless resource. This can allow the exemplary UE to keepradios in an off or low-power state until a local wireless resource isdetermined to be available based on an RSSI snapshot matching historicalRSSI information, which can result in reduced energy consumption andimproved battery performance for the UE. An exemplary snapshot analysiscomponent can be a processor of a mobile phone executing snapshotanalysis instructions, a dedicated circuit in a mobile device performingsnapshot analysis, etc. It will be noted that numerous other examples ofsnapshot analysis components can be recited but are not herein for thesake of clarity and brevity.

In another embodiment, the analysis can compare the RSSI snapshot tohistorical information to determine a probability of a local wirelessresource being available. The probability can be based on similaritiesand differences between the RSSI snapshot and the historical RSSIinformation. As an example, where the RSSI snapshot has 100 RSSI valuesand 99 match historical RSSI information, there can be a highprobability of any correlated local wireless resources being available.As a second example, where the RSSI snapshot has two RSSI values andthey are an order of magnitude different form the historical RSSIinformation a low probability of any correlated local wireless resourcesbeing available can be determined. As a third example, where five RSSIvalues compose the snapshot and they are each within 3% of a historicalset of RSSI information, a moderately high probability for a relatedlocal wireless resource being available can be determined. As a fourthexample, if the snapshot contains RSSI values strongly in accord withhistorical RSSI information and there is no correlated local wirelessresource, a very low probability of availability can be determined Ofnote, numerous other examples are within the present scope despite notbeing enumerated for clarity and brevity.

FIG. 2 is a depiction of a system 200, which can facilitate RSSIsnapshot analysis in accordance with aspects of the subject disclosure.System 200 can include RSSI-ICC 210. RSSI-ICC 210 can facilitatecapturing RSSI information. RSSI information can include RSSIs for aplurality of received RF signals. The RSSI information can be a set ofRSSIs for one or more received RF signals or can be an empty set, suchas where there are no received RF signals. For instance, the RSSIinformation can be a set of RSSIs for three NodeBs. The RSSI informationreceived with the assistance of RSSI-ICC 210 can be independent ofactual location information.

System 200 can also include RSSI-HIC 220. RSSI-HIC 220 can assist system200 to receive historical RSSI information. Historical RSSI informationcan include one or more historic RSSI snapshots. Historical RSSIinformation can also include other RSSI information such asidentification information of historically received RF signals,identification information for correlated local wireless resources, etc.In an embodiment, historical RSSI information can include anyinformation that can facilitate an analysis of a current RSSI snapshotto determine a probability related to the availability of a localwireless resource. As an example, historic RSSI information can includea set of historic RSSIs for historically received RF signals, a serviceset identifier (SSID) correlated to the historic RSSI information, e.g.,a name that identifies a particular local wireless resource correlatedwith the historic RSSI information, and a date corresponding to the lasttime the SSID was correlated to the historic RSSI information.

RSSI-ICC 210 and RSSI-HIC 220 can be communicatively coupled to snapshotanalysis component 230. Snapshot analysis component 230 can analyze aRSSI snapshot to determine a probability that a local wireless resourceis available. In an embodiment, this analysis can be based, in part, onreceived historical RSSI information, e.g., received by way of RSSI-HIC220. Snapshot analysis component 230 can further include decision enginecomponent 232 that can facilitate forming determinations relating to adetermining a probability that a local wireless resource is available.Determinations can include satisfying a snapshot analysis rule, notsatisfying a snapshot analysis rule, satisfying part of a snapshotanalysis rule, applying a snapshot analysis rule to a set ofinformation, etc. A determination relating to a snapshot analysis rulecan be related to historical RSSI information. As a first example, wherea snapshot analysis rule is satisfied when a current RSSI snapshot ismatches at least 95% a set of received historical RSSI information,decision engine component 232 can determine that this rule is satisfiedby comparing a RSSI snapshot to at least one set of historic RSSIinformation. As a further example, decision engine component 232 canapply a weighting rule to snapshot analysis, such as where a weightingrule indicates that a weighting factor of 10× is to be applied to setsof historic RSSI information less than one hour old, e.g., wherehistoric RSSI information is newer it can be associated with a greaterweight in determining a probability that a local wireless resource is,or is not, available. Numerous other examples of specific snapshotanalysis rules are not explicitly recited for brevity but are to beconsidered within the scope of the present disclosure.

In an aspect, decision engine component 232 can include rule component234 to facilitate forming determinations related to a snapshot analysisrule. Rule component 234 can facilitate employing one or more snapshotanalysis rules. These snapshot analysis rules can include rules fordetermining a probability relating to the availability of a localwireless resource. As an example, a rule can indicate that a localwireless resource must be strongly correlated with a set of historicalRSSI information to be included as an available local wireless resourcewherein a RSSI snapshot is sufficiently similar to the set of historicalRSSI information to be deemed to match. In an embodiment, rule component234 can be a rule engine that allows the application of logicaldeterminations to be embodied in one or more algorithms related tosnapshot analysis. As non-limiting examples, rule component 234 cangenerate a snapshot analysis rule, modify a snapshot analysis rule,delete a snapshot analysis rule, select a snapshot analysis rule asrelevant/irrelevant, merge or diverge other snapshot analysis rules,etc.

FIG. 3 illustrates a system 300, which facilitates RSSI snapshotanalysis employing an inference in accordance with aspects of thesubject disclosure. System 300 can include RSSI-ICC 310. RSSI-ICC 310can facilitate capturing RSSI information. The RSSI information can be aset of RSSIs for one or more received RF signals or can be an empty set,such as where there are no received RF signals. The RSSI informationreceived with the assistance of RSSI-ICC 210 can be independent ofactual location information. System 300 can also include RSSI-HIC 320.RSSI-HIC 320 can assist system 300 to receive historical RSSIinformation. Historical RSSI information can include any informationthat can facilitate an analysis of a current RSSI snapshot to determinea probability related to the availability of a local wireless resource.

RSSI-ICC 310 and RSSI-HIC 320 can be communicatively coupled to snapshotanalysis component 330. Snapshot analysis component 330 can analyze aRSSI snapshot to determine a probability that a local wireless resourceis available. This analysis can be based, in part, on receivedhistorical RSSI information. Snapshot analysis component 330 can furtherinclude decision engine component 332 that can facilitate formingdeterminations relating to a determining a probability that a localwireless resource is available. Decision engine component 332 caninclude rule component 334 to facilitate forming determinations relatedto a snapshot analysis rule. Rule component 334 can facilitate employingone or more snapshot analysis rules. These snapshot analysis rules caninclude rules for determining a probability relating to the availabilityof a local wireless resource.

Snapshot analysis component 330 can further include inference component336. Inference component 336 can receive an inference relating to theprobability that a local wireless resource is available. In anembodiment, inference component 336 can facilitate automating one ormore features in accordance with the subject innovation. Various machinelearning and reasoning (MLR) based schemes for carrying out aspects ofthe disclosed subject matter can be employed. For example, a process fordetermining a probability that a local wireless resource is availablecan be facilitated by way of an automatic classifier system and process.

A classifier can be a function that maps an input attribute vector,x=(x1, x2, x3, x4, xn), to a class label class(x). The classifier canalso output a confidence that the input belongs to a class, that is,f(x)=confidence(class(x)). Such classification can employ aprobabilistic and/or other statistical analysis, e.g., one factoringinto the analysis the similarity or dissimilarity between the RSSIsnapshot and the historical RSSI information, to prognose or infer anaction that a user can desire to be automatically performed.

As used herein, terms “to infer” and “inference” refer generally to theprocess of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured by wayof events and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs that splits the triggering input events from thenon-triggering events in an optimal way. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, e.g., naïve Bayes, Bayesian networks, decisiontrees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also is inclusive ofstatistical regression that is utilized to develop models of ranking orpriority.

As will be readily appreciated from the subject specification, thepresently disclosed subject matter can employ classifiers that areexplicitly trained (e.g., via a generic training data) as well asimplicitly trained (e.g., via observing user behavior, receivingextrinsic information). For example, SVM's can be configured by way of alearning or training phase. Thus, the classifier can be employed toautomatically learn and perform a number of functions according topredetermined criteria.

In one example, inference component 336 can monitor actual availablelocal wireless resources in light of determined probabilities of thoselocal wireless resources being available to learn and reason aboutpatterns of availability with respect to RSSI snapshot analysis. Inanother example, learning and reasoning can be applied to informationgleaned from other sources indicating newly added local wirelessresources by affiliation with existing local wireless resources that canbe associated with historical RSSI information, for instance, an officecan deploy a new Wi-Fi network, e.g., a second network, to decrease theburden on an existing Wi-Fi network that is already associated with aset of historical RSSI information. As a further example, learning canbe predicated on tracking of user behavior, for instance, where a usermanually enables a Wi-Fi radio on a UE, this can be correlated with aRSSI snapshot. This can facilitate learning and future inferences thatthe RSSI snapshot is associated with one or more local wirelessresources.

In yet another example, inference component 336 can facilitate learningand reasoning about changes in user interest, intentions, needs, andgoals over time, thereby affecting changes in predicting theavailability of a local wireless resource. As an example, a user canselect local wireless resources with high bandwidth levels but ignorelocal wireless resources with low bandwidth levels. This can lead tomodifying the probability of an available local wireless resource toinclude only high bandwidth level wireless resources as available andtreating low bandwidth level wireless resources as unavailable, e.g.,learning that wireless resources not meeting a predetermined thresholdbandwidth can be treated as unavailable. These are only but a fewexamples of the capabilities provided by inference component 336, andare not to be construed as limiting in any way.

Machine learning algorithms have a set of inputs, and produce an output(typically a probability). They can be trained up using “training data”and then can be run on test data. For example, 10,000 examples of RSSIsnapshots and 10,000 examples of correlated available local wirelessresources can be employed. For each of these 20,000 examples, the systemcan be given a set of inputs, for instance, sets of exemplary historicalRSSI information. Large samples of high probability availability of alocal wireless resource can be obtained from location-based services,e.g., those using mapping of local wireless resources to locationsderived from RSSI information, such as databases of available Wi-Fihotspots by location correlated to RSSI information.

FIG. 4 is illustrates an exemplary system 400 including RSSI snapshotanalysis in accordance with aspects of the subject disclosure. System400 can include NodeBs 498A-D. The NodeBs 498 A-D Wi-Fi hotspot 480 canprovide wireless access through area 481. Building 482 can causeattenuation of RF signals passing through it.

UE 472 can receive RSSI information for RF signals, as indicated by thesolid bolts, associated with NodeBs 498B-D. This RSSI information can beincluded in an RSSI snapshot of UE 472. The RSSI snapshot of UE 472 canbe associated with the availability of Wi-Fi hotspot 480 wherein UE 472is within the range 481 of Wi-Fi hotspot spot 480. The RSSI informationof the RSSI snapshot can be associated with the geometric orientationand environmental conditions of UE 472 with respect to NodeBs 498B-D butneed not be associated with any particular location or mapping of UE472.

UE 474 can also receive RSSI information, as indicated by the empty,broken-border-lined bolts, from NodeBs 498B-D. This RSSI information canbe included in an RSSI snapshot of UE 474. The RSSI snapshot of UE 474can receive the RSSI snapshot of UE 472 as historical RSSI information.This historical RSSI information can include the particular RSSIsresulting from the particular geometric orientation and environmentalconditions of UE 472 at the time the historical RSSI snapshot wascaptured. The historical RSSI information can also include theassociation with any local wireless resources, e.g., Wi-Fi hotspot 480.UE 474 can include components of a system that can be the same as, orsimilar to, system 100, 200, or 300. As such, UE 474 can analyze theRSSI snapshot of UE 474 with regard to the historical RSSI informationassociated with UE 472 to determine a probability that a local wirelessresource is available.

The analysis of the RSSI snapshot of UE 474 with regard to thehistorical RSSI information associated with UE 472 can indicate that UE474 is not similarly geometrically oriented, in the same environmentalconditions, or a combination thereof. Based on this determination, aprobability can be determined as to the availability of Wi-Fi hotspot480 for UE 474. The analysis can be based on one or more snapshotanalysis rules as disclosed herein. For instance, it can be determinedthat UE 474 is receiving RF signals from the same NodeBs as UE 472,e.g., NoedBs 498B-D, but that the RSSI values are sufficiently differentthat there is a low probability that Wi-Fi hotspot 480 is available toUE 474. The differences in the RSSI values can be a result of differentgeometric orientation and/or different environmental conditions.However, where the present disclosure is ignorant of the actual locationof the exemplary UE, it is not possible to differentiate the cause ofthe differences in the RSSIs comprising the RSSI snapshot for UE 474.Based on this exemplary determination of a low probability of theavailability of Wi-Fi hotspot 480, UE 474 can cause a Wi-Fi radio of UE474 (not illustrated) to remain or go into an off/low power condition toconserve battery life. Where the difference in RSSIs for the RSSIsnapshot of UE 474 is actually at least partly related to UE 474 beingin a different geometric orientation, as illustrated in system 400, theprobability can be associated with a correct result.

In contrast, similar conditions can exist for UE 470. That is, UE 470can also receive RSSI information, as indicated by the empty,solid-border-lined bolts, from NodeBs 498B-D. This RSSI information canbe included in an RSSI snapshot of UE 470. The RSSI snapshot of UE 470can receive the RSSI snapshot of UE 472 as historical RSSI information.This historical RSSI information can include the particular RSSIsresulting from the particular geometric orientation and environmentalconditions of UE 472 at the time the historical RSSI snapshot wascaptured. The historical RSSI information can also include theassociation with any local wireless resources, e.g., Wi-Fi hotspot 480.UE 470 can include components of a system that can be the same as, orsimilar to, system 100, 200, or 300. As such, UE 470 can analyze theRSSI snapshot of UE 470 with regard to the historical RSSI informationassociated with UE 472 to determine a probability that a local wirelessresource is available.

The analysis of the RSSI snapshot of UE 470 with regard to thehistorical RSSI information associated with UE 472 can indicate that UE470 is not similarly geometrically oriented, in the same environmentalconditions, or a combination thereof. Based on this determination, aprobability can be determined as to the availability of Wi-Fi hotspot480 for UE 470. The analysis can be based on one or more snapshotanalysis rules as disclosed herein. For instance, it can be determinedthat UE 470 is receiving RF signals from some of the same NodeBs as UE472, e.g., NoedBs 498B and 498 C, and also receiving RF signals from adifferent NodeB, e.g., Node B 498A. In some embodiments, snapshotsincluding different RF sources can affect a determination of aprobability that a local wireless resource is available. In thisexample, where there are two of the three same RF sources, e.g., NodeBs498B and 498C, the RSSI values can be sufficiently different that thereis a low probability that Wi-Fi hotspot 480 is available to UE 470. Thedifferences in the RSSI values can be a result of different geometricorientation and/or different environmental conditions. However, wherethe present disclosure is ignorant of the actual location of theexemplary UE, it is not possible to differentiate the cause of thedifferences in the RSSIs comprising the RSSI snapshot for UE 470. Basedon this exemplary determination of a low probability of the availabilityof Wi-Fi hotspot 480, UE 470 can cause a Wi-Fi radio of UE 470 (notillustrated) to remain or go into an off/low power condition to conservebattery life. Where the difference in RSSIs for the RSSI snapshot of UE470 is actually at least partly related to UE 470 being in a differentgeometric orientation, as illustrated in system 400, the probability canbe associated with an incorrect result, e.g., UE 470 is actually withinthe area of coverage 481 for Wi-Fi hotspot 480.

In an embodiment, where enough historical RSSI information is amassed,it can be appreciated that the boundaries of region 481 associated withwireless resource coverage from Wi-Fi hotspot 480 can be associated witha plurality of sets of historical RSSI information to provide anyalgorithms computing a probability with a better chance of converging ona solution that is correct rather than incorrect. For example, wherethere is a set of historical RSSI information for large numbers ofgeometric orientations if UEs within region 481, it becomes lessmathematically likely that the differences between a RSSI snapshot and aset of historical RSSI information will be a result of differences ingeometric orientation. Where environmental conditions are also highlysampled for each geometric orientation, it is also likely thatdifferences between a RSSI snapshot and historical RSSI information canbe better characterized. Huge data stores of historical RSSI informationare therefore likely to provide for an acceptable level of accuracy whendetermining a probability that a local wireless resource is available.

Moreover, in some embodiments, e.g., those that include inferencecomponents, training and learning can improve accuracy and reduce thelikelihood of a probability being associated with an incorrect result.As an example, where UE 470 and/or UE 474 both scan for available Wi-Finetworks when capturing a RSSI snapshot, for instance in a ‘learningmode’, the probability can be validated. That is, where the probabilityfor UE 474 was that a local wireless resource was not available, thiscan be confirmed by scanning for Wi-Fi hotspot 480 and not finding it.This information can be added to the data store of historical RSSIinformation to be used for later determinations. This information canalso be employed as training data for an inference component (notillustrated). Similarly, the incorrect result for UE 470 can be notedand included in the historical RSSI information database and/or used forclassifier training. It will be noted that this ‘learning mode’ would beassociated with power consumption for scanning a Wi-Fi radio of the UEsto validate the probability determination. It will also be noted thatthere is no need to determine the location of any UE in system 400 forthe determination of a probability that a local wireless resource isavailable based on the analysis of an RSSI snapshot in view ofhistorical RSSI information.

FIG. 5 illustrates an exemplary system 500 including inference basedRSSI snapshot analysis in. System 500 can include building 582 that canattenuate RF signals as illustrated. UE 570 can receive RSSI informationfor RF signals, as indicated by the broken line arrows, associated withNodeBs 598A-D. This RSSI information can be included in an RSSI snapshotof UE 570. The RSSI snapshot of UE 570 can be associated with theavailability of local wireless resources (not illustrated). The RSSIinformation of the RSSI snapshot can be associated with the geometricorientation and environmental conditions of UE 570 with respect toNodeBs 498A-D but need not be associated with any particular location ormapping of UE 570.

UE 572 can also receive RSSI information, as indicated by the solid-linearrows, from NodeBs 498A-C. This RSSI information can be included in anRSSI snapshot of UE 572. The RSSI snapshot of UE 572 can receive theRSSI snapshot of UE 570 as historical RSSI information. This historicalRSSI information can include the particular RSSIs resulting from theparticular geometric orientation and environmental conditions of UE 570at the time the historical RSSI snapshot was captured. The historicalRSSI information can also include the association with any localwireless resources. UE 572 can include components of a system that canbe the same as, or similar to, system 300. As such, UE 572 can analyzethe RSSI snapshot of UE 572 with regard to the historical RSSIinformation associated with UE 570 to determine a probability that alocal wireless resource is available.

The analysis of the RSSI snapshot of UE 572 with regard to thehistorical RSSI information associated with UE 570 can indicate that UE572 is not identically geometrically oriented, in the same environmentalconditions, or a combination thereof. Based on this determination, aprobability can be determined as to the availability of any localwireless resources for UE 572. The analysis can be based on one or moresnapshot analysis rules as disclosed herein. For instance, it can bedetermined that UE 572 is receiving RF signals from three of the foursame NodeBs as UE 570, e.g., NoedBs 498A-C. This condition can beincluded in determine the probability. Further, where exemplary UE 572includes an inference component (not illustrated) that is the same as,or similar to, that inference component 336 of system 300, an inferencecan be made as to the probability determination.

The inference can be influenced by training of the inference componentor on learning that the inference component may have experience, e.g.,by additional training in a ‘learning mode’ as disclosed herein. As afirst example, an inference can be that where three of the four RFsources from historical RSSI information are present in a RSSI snapshotfor UE 572, that the missing RF source can be of minimal importance andas such, this factor should be associated with a weighting factor tominimize the impact of this factor. This inference can be based on themagnitude of the difference between the snapshot and historical RSSIinformation for the remaining RF sources, e.g., where the differences donot transition a threshold value, the missing RF source in minimallyimportant while if the difference do transition the threshold value thenthe missing RF source can be more substantially important. This canaccount, for example, when an object moves between a RF source and a UEblocking just one of the RF sources while there is no substantial changein the RSSI values for the remaining RF sources. In this example, therewould likely not be a change in any available local wireless resourceand the impact of the loss of one of the RF sources with little changein the remaining RF sources should not overwhelm a determination of aprobability as disclosed. Similarly, where one RF source is lost and theRSSI values of the remaining RF sources changes substantially, such aswhen a UE might enter a building or tunnel, this can be learned to bemore likely associated with a change in the availability of localwireless resources and can therefore be associated with an increasedweighting of this factor.

A further exemplary inference can be temporally based. A temporalinference can apply, for instance, a weighting factor with a greatereffect on determining a probability as the more time elapses between anRSSI snapshot and historical RSSI information. This example can beappreciated by noting that where a large change in RSSI values occursover a short time period it can be associated with more drastic changesin either geometric orientation or environmental conditions. As anexample, where a loss of 5 dB in RSSI is found when analyzing a RSSIsnapshot in view of historical RSSI information that is three weeks old,that this loss may be simply due to weather conditions and that theprobability should be less influenced by these older factors. Incontrast, where a 5 dB loss is found in view of historic RSSIinformation that is only 30 seconds old, there can be an inference thatthe loss is due to a change in geometric orientation or a substantialenvironmental change, and that this should more substantially impactdetermination of a probability of a local wireless resource beingavailable.

As illustrated in system 500, the geometric orientation of UE 572 withregard to UE 570 can be minimal, e.g., the loss of RF signal from NodeB598D is simply a result of UE 572 being in the RF shadow of building582. In system 500, an inference can be that minimal changes in the RSSIvalues form the remaining RF sources, e.g., NodeB 598A-C can indicatethat loss of RF signal from NodeB 598D are to be minimized by applying aweighting factor when determining a probability that a local wirelessresource is available.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 6-FIG. 8. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methodologies.Furthermore, not all illustrated acts may be required to implement adescribed example method in accordance with the subject specification.Further yet, two or more of the disclosed example methods can beimplemented in combination with each other, to accomplish one or moreaspects herein described. It should be further appreciated that theexample methods disclosed throughout the subject specification arecapable of being stored on an article of manufacture (e.g., acomputer-readable medium) to allow transporting and transferring suchmethods to computers for execution, and thus implementation, by aprocessor or for storage in a memory.

FIG. 6 illustrates aspects of a method 600 facilitating RSSI snapshotanalysis in accordance with aspects of the subject disclosure. At 610,RSSI information for a mobile device can be received. The RSSIinformation can include a set of RSSI values for one or more RF signalsor can be an empty set. The RSSI information received can be independentof actual location information. The RSSI information can be included inan RSSI snapshot associated with the mobile device, as disclosed herein.

At 620, historical RSSI information can be received. This historicalRSSI information can be correlated to a local wireless resource.Historical RSSI information can include one or more historic RSSIsnapshots. Historical RSSI information can also include other RSSIinformation such as identification information of historically receivedRF signals, identification information for correlated local wirelessresources, etc. Historical RSSI information can include any informationthat can facilitate an analysis of a current RSSI snapshot to determinea probability related to the availability of a local wireless resource.

At 630, a probability that the local wireless resource is available canbe determined This determination can be based on an analysis of the RSSIinformation, e.g., the RSSI snapshot, and the historical RSSIinformation. At this point, method 600 can end. In an aspect, instantRSSI information can be compared to histrionic RSSI information. Wherethe instant RSSI information matches a set of historic RSSI informationor satisfies one or more rules relating to the comparison, a level ofconfidence can be associated with the mobile device being in the same,or a similar, geometric orientation and/or in the same, or similar,environmental conditions as a UE associated with the historic RSSIinformation. Thus, where a local wireless resource was available for theUE associated with the historic RSSI information, said same localwireless resource can similarly be available to the mobile device. Thecomparison of an RSSI snapshot to historic RSSI information in method600 can be independent of any conversion of RSSI data into locationinformation or map information.

FIG. 7 illustrates aspects of a method 700 for changing the state of awireless radio based on RSSI snapshot analysis in accordance withaspects of the subject disclosure. At 710, RSSI information for a mobiledevice can be received. The RSSI information can include a set of RSSIvalues for one or more RF signals or can be an empty set. The RSSIinformation received can be independent of actual location information.The RSSI information can be included in an RSSI snapshot associated withthe mobile device, as disclosed herein.

At 720, historical RSSI information can be received. This historicalRSSI information can be correlated to a local wireless resource.Historical RSSI information can include one or more historic RSSIsnapshots. Historical RSSI information can also include other RSSIinformation such as identification information of historically receivedRF signals, identification information for correlated local wirelessresources, etc. Historical RSSI information can include any informationthat can facilitate an analysis of a current RSSI snapshot to determinea probability related to the availability of a local wireless resource.

At 730, a probability that the local wireless resource is available canbe determined This determination can be based on an analysis of the RSSIinformation and the historical RSSI information. In an embodiment, theinstant RSSI information can be analyzed in light of the receivedhistrionic RSSI information. Where the instant RSSI information matchesa set of historic RSSI information or satisfies one or more snapshotanalysis rules, a probability can be determined that a local wirelessresource is available to the mobile device. The comparison of an RSSIsnapshot to historic RSSI information in method 700 can be independentof any conversion of RSSI data into location information or mapinformation.

At 740, a state of a wireless radio of the mobile device can be changedbased on the probability that the local wireless resource is available.At this point, method 700 can end. In an aspect, where a wireless radioof a mobile device consumes power when enabled, it can be desirable toplace the wireless radio in a disabled state associated with lower powerconsumption when the wireless radio is not in use. As such, where alocal wireless resource is not available, having the wireless radio ofthe mobile device enabled is superfluous. Therefore, where the RSSIsnapshot analysis indicates that a local wireless resource is not likelyavailable, a determination to disable, or keep disabled, the wirelessradio can be employed to conserve battery power. Similarly, where alocal wireless resource is available, the wireless radio can be enabledto take advantage of the resource where otherwise desirable. Thedetermined probability can be employed as a metric when selecting anenabled or disable sate for a wireless radio of the mobile device bymethod 700 or similar methods.

It will be noted that method 700 can be combined with other methods andtechniques to enable advanced wireless radio state selection. As anexample, method 700 can facilitate determining a probability that if alocal wireless resource is available. Another method can determine ifaccess to a local wireless resource is desirable, for instance, based onan amount of data to be transmitted, the traffic level of other wirelessresources such as cellular traffic, etc. Where the probability valuetransitions a predetermined value and access to the local wirelessresource is desirable as determined by the complimentary method, awireless radio state can be enabled for the mobile device. Similar typesof compound analyses can be conducted to set a wireless radio state asdisabled or off to conserve power when no network is likely availableand/or there is a sufficiently low need for the use of such a localwireless resource. It will be noted that numerous other examples fallwithin the present scope despite not be expressly disclosed for the sakeof clarity and brevity.

FIG. 8 illustrates a method 800 facilitating RSSI snapshot analysisbased on inference in accordance with aspects of the subject disclosure.At 810, RSSI information for a mobile device can be received. The RSSIinformation can include a set of RSSI values for one or more RF signalsor can be an empty set. The RSSI information received can be independentof actual location information. The RSSI information can be included inan RSSI snapshot associated with the mobile device, as disclosed herein.

At 820, historical RSSI information can be received. This historicalRSSI information can be correlated to a local wireless resource.Historical RSSI information can include one or more historic RSSIsnapshots. Historical RSSI information can also include other RSSIinformation such as identification information of historically receivedRF signals, identification information for correlated local wirelessresources, etc. Historical RSSI information can include any informationthat can facilitate an analysis of a current RSSI snapshot to determinea probability related to the availability of a local wireless resource.

At 830, a level of similarity between the instant RSSI information andthe historical RSSI information can be determined. This determinationcan be based on an analysis of the RSSI information and the historicalRSSI information. In an embodiment, the instant RSSI information can beanalyzed in light of the received histrionic RSSI information. Theinstant RSSI information can be the same as, or is similar to, a set ofhistoric RSSI information and this level of similarity can bequantified. The quantification can be based on one or more rulesrelating to comparison of RSSI information. The comparison of an RSSIsnapshot to historic RSSI information in method 800 can be independentof any conversion of RSSI data into location information or mapinformation.

At 840, an inference can be formed relating to the availability of alocal wireless resource. This inference can be based on the determinedlevel of similarity between the RSSI snapshot and the historical RSSIinformation, wherein the historical RSSI information can includecorrelated local wireless resource information. In an aspect, where anRSSI snapshot is more similar to a set of historical RSSI information,it can be more likely that a local wireless resource associated with theset of historical RSSI information will also be available to the mobiledevice receiving the RSSI snapshot. Correspondingly, an inference can bethat the more dissimilar an RSSI snapshot from a set of historical RSSIinformation, the less likely there is to be a local wireless resourceavailable that was correlated to the historic RSSI information.

In an aspect, if an actual photo of an instant set of RSSI informationcould be taken and laid against photos of historical sets of RSSIinformation, an inference could be that the closer the match between theinstant and historical photos the more similarities they are likely toshare. Where an instant photo and a historical photo are sufficientlysimilar, it can be inferred that if the historic photo includes a Wi-Finetwork, then the instant photo will also have the same Wi-Fi network.This exemplary ‘photo-lineup’ does not require that the locations of thephotos ever be determined because the comparison itself is based on thecontent of the photos and how they compare to one another rather than towhere they are taken. For instance, as an analogy, a historical photo ofthe Empire State building can be highly correlated with an instant photoof the Empire State building even where the photographic angles aredifferent, the lighting is different, the weather on the days of thephoto is different, etc. Further, it is unimportant where the EmpireState building is located to determine that the two photos aresufficiently similar and likely are photos of the same building. Whereit is known that the Empire State building has 73 elevators in thehistoric photo, it can be inferred that the building in the instantphoto also has 73 elevators. It will be noted that the more dissimilarthe parameters of the instant photo are form the historic photo of theEmpire State building, the less likely it can be to draw a conclusionthat the two pictured buildings are the same and therefor there can be alower probability that the building in the instant photo has 73elevators.

At 840, a state of a wireless radio of the mobile device can be changedbased on the inference. At this point, method 800 can end. In an aspect,where a wireless radio of a mobile device consumes power when enabled,it can be desirable to place the wireless radio in a disabled stateassociated with lower power consumption when the wireless radio is notin use. The inference can be employed as a metric when selecting anenabled or disable sate for a wireless radio of the mobile device bymethod 800 or similar methods.

FIG. 9 presents an example embodiment 900 of a mobile network platform910 that can implement and exploit one or more aspects of the subjectmatter described herein. Generally, wireless network platform 910 caninclude components, e.g., nodes, gateways, interfaces, servers, ordisparate platforms, that facilitate both packet-switched (PS) (e.g.,internet protocol (IP), frame relay, asynchronous transfer mode (ATM))and circuit-switched (CS) traffic (e.g., voice and data), as well ascontrol generation for networked wireless telecommunication. As anon-limiting example, wireless network platform 910 can be included aspart of a telecommunications carrier network. Mobile network platform910 includes CS gateway node(s) 912 which can interface CS trafficreceived from legacy networks like telephony network(s) 940 (e.g.,public switched telephone network (PSTN), or public land mobile network(PLMN)) or a signaling system #7 (SS7) network 970. Circuit switchedgateway node(s) 912 can authorize and authenticate traffic (e.g., voice)arising from such networks. Additionally, CS gateway node(s) 912 canaccess mobility, or roaming, data generated through SS7 network 970; forinstance, mobility data stored in a visited location register (VLR),which can reside in memory 930. Further, RSSI information can be storedin memory 930. Moreover, CS gateway node(s) 912 interfaces CS-basedtraffic and signaling and PS gateway node(s) 918. As an example, in a3GPP UMTS network, CS gateway node(s) 912 can be realized at least inpart in gateway GPRS support node(s) (GGSN). It should be appreciatedthat functionality and specific operation of CS gateway node(s) 912, PSgateway node(s) 918, and serving node(s) 916, can be provided anddictated by radio technology(ies) utilized by mobile network platform910 for telecommunication.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 918 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions caninclude traffic, or content(s), exchanged with networks external to thewireless network platform 910, like wide area network(s) (WANs) 950,enterprise network(s) 970, and service network(s) 980, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 910 through PS gateway node(s) 918. It is to benoted that WANs 950 and enterprise network(s) 960 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) 917,packet-switched gateway node(s) 918 can generate packet data protocolcontexts when a data session is established; other data structures thatfacilitate routing of packetized data also can be generated. To thatend, in an aspect, PS gateway node(s) 918 can include a tunnel interface(e.g., tunnel termination gateway (TTG) in 3GPP UMTS network(s) (notshown)) which can facilitate packetized communication with disparatewireless network(s), such as Wi-Fi networks.

In embodiment 900, wireless network platform 910 also includes servingnode(s) 916 that, based upon available radio technology layer(s) withintechnology resource(s) 917, convey the various packetized flows of datastreams received through PS gateway node(s) 918. It is to be noted thatfor technology resource(s) 917 that rely primarily on CS communication,server node(s) can deliver traffic without reliance on PS gatewaynode(s) 918; for example, server node(s) can embody at least in part amobile switching center. As an example, in a 3GPP UMTS network, servingnode(s) 916 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)914 in wireless network platform 910 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can include add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bywireless network platform 910. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 918 for authorization/authentication and initiation of a datasession, and to serving node(s) 916 for communication thereafter. Inaddition to application server, server(s) 914 can include utilityserver(s), a utility server can include a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through wireless network platform 910 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 912and PS gateway node(s) 918 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 950 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to wirelessnetwork platform 910 (e.g., deployed and operated by the same serviceprovider), such as femto-cell network(s) (not shown) that enhancewireless service coverage within indoor confined spaces and offload RANresources in order to enhance subscriber service experience within ahome or business environment.

It is to be noted that server(s) 914 can include one or more processorsconfigured to confer at least in part the functionality of macro networkplatform 910. To that end, the one or more processor can execute codeinstructions stored in memory 930, for example. It should be appreciatedthat server(s) 914 can include a content manager 915, which operates insubstantially the same manner as described hereinbefore.

In example embodiment 900, memory 930 can store information related tooperation of wireless network platform 910. Other operationalinformation can include provisioning information of mobile devicesserved through wireless platform network 910, subscriber databases;application intelligence, pricing schemes, e.g., promotional rates,flat-rate programs, couponing campaigns; technical specification(s)consistent with telecommunication protocols for operation of disparateradio, or wireless, technology layers; and so forth. Memory 930 can alsostore information from at least one of telephony network(s) 940, WAN950, enterprise network(s) 960, or SS7 network 970. In an aspect, memory930 can be, for example, accessed as part of a data store component oras a remotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules include routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can include both volatile andnonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory, forexample, can be included in volatile memory 1020, non-volatile memory1022 (see below), disk storage 1024 (see below), and memory storage 1046(see below). Further, nonvolatile memory can be included in read onlymemory (ROM), programmable ROM (PROM), electrically programmable ROM(EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatilememory can include random access memory (RAM), which acts as externalcache memory. By way of illustration and not limitation, RAM isavailable in many forms such as synchronous RAM (SRAM), dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM(DRRAM). Additionally, the disclosed memory components of systems ormethods herein are intended to comprise, without being limited tocomprising, these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, includingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, watch, tablet computers, . . . ),microprocessor-based or programmable consumer or industrial electronics,and the like. The illustrated aspects can also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network;however, some if not all aspects of the subject disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

FIG. 10 illustrates a block diagram of a computing system 1000 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1012 includes a processing unit 1014, a systemmemory 1016, and a system bus 1018. In an embodiment, computer 1012 canbe all or part of the hardware comprising a snapshot analysis componentsuch as component 130, 230 or 330. In a further embodiment, computer1012 can be part of a UE configured to perform snapshot analysis. Systembus 1018 couples system components including, but not limited to, systemmemory 1016 to processing unit 1014. Processing unit 1014 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as processing unit1014.

System bus 1018 can be any of several types of bus structure(s)including a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics, VESA Local Bus (VLB), PeripheralComponent Interconnect (PCI), Card Bus, Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1194), and SmallComputer Systems Interface (SCSI).

System memory 1016 includes volatile memory 1020 and nonvolatile memory1022. A basic input/output system (BIOS), containing routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can includeROM, PROM, EPROM, EEPROM, or flash memory. Volatile memory 1020 includesRAM, which acts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as SRAM, dynamic RAM(DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM),enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM(RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM(RDRAM).

Computer 1012 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1024 can include storage media separately or in combination with otherstorage media including, but not limited to, an optical disk drive suchas a compact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive),CD rewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 1024 tosystem bus 1018, a removable or non-removable interface can be used,such as interface 1026. In an embodiment, disk storage 1024 can storehistoric RSSI information to facilitate analysis of a RSSI snapshot. Inanother embodiment, disk storage 1024 can store RSSI snapshotinformation. In a further embodiment, determined probabilities relatingto the availability of a local wireless resource can be stored on diskstorage 1024.

Computing devices can include a variety of media, which can includecomputer-readable storage media or communications media, which two termsare used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules, or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

It can be noted that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software includes an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. Input devices 1036 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, cellphone, smartphone, tablet computer, etc. These and other input devicesconnect to processing unit 1014 through system bus 1018 by way ofinterface port(s) 1038. Interface port(s) 1038 include, for example, aserial port, a parallel port, a game port, a universal serial bus (USB),an infrared port, a Bluetooth port, an IP port, or a logical portassociated with a wireless service, etc. Output device(s) 1040 use someof the same type of ports as input device(s) 1036.

Thus, for example, a USB port can be used to provide input to computer1012 and to output information from computer 1012 to an output device1040. Output adapter 1042 is provided to illustrate that there are someoutput devices 1040 like monitors, speakers, and printers, among otheroutput devices 1040, which use special adapters. Output adapters 1042include, by way of illustration and not limitation, video and soundcards that provide means of connection between output device 1040 andsystem bus 1018. It should be noted that other devices and/or systems ofdevices provide both input and output capabilities such as remotecomputer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, a workstation, a microprocessor based appliance, apeer device, or other common network node and the like, and can includemany or all of the elements described relative to computer 1012.

For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 can belogically connected to computer 1012 through a network interface 1048and then physically connected by way of communication connection 1050.Network interface 1048 encompasses wire and/or wireless communicationnetworks such as local-area networks (LAN) and wide-area networks (WAN).LAN technologies include Fiber Distributed Data Interface (FDDI), CopperDistributed Data Interface (CDDI), Ethernet, Token Ring and the like.WAN technologies include, but are not limited to, point-to-point links,circuit switching networks like Integrated Services Digital Networks(ISDN) and variations thereon, packet switching networks, and DigitalSubscriber Lines (DSL). As noted below, wireless technologies may beused in addition to or in place of the foregoing.

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can include, for example, internaland external technologies such as modems, including regular telephonegrade modems, cable modems and DSL modems, ISDN adapters, and Ethernetcards.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Processors can exploit nano-scale architectures suchas, but not limited to, molecular and quantum-dot based transistors,switches, and gates, in order to optimize space usage or enhanceperformance of user equipment. A processor may also be implemented as acombination of computing processing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which can be operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can include a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point (AP),” “basestation,” “Node B,” “evolved Node B (eNode B),” “home Node B (HNB),”“home access point (HAP),” and the like, are utilized interchangeably inthe subject application, and refer to a wireless network component orappliance that serves and receives data, control, voice, video, sound,gaming, or substantially any data-stream or signaling-stream to and froma set of subscriber stations or provider enabled devices. Data andsignaling streams can include packetized or frame-based flows.

Additionally, the term “core-network”, “core”, “core carrier network”,or similar terms can refer to components of a telecommunications networkthat provide some or all of aggregation, authentication, call controland switching, charging, service invocation, or gateways. Aggregationcan refer to the highest level of aggregation in a service providernetwork wherein the next level in the hierarchy under the core nodes canbe the distribution networks and then the edge networks. UEs do notnormally connect directly to the core networks of a large serviceprovider but can be routed to the core by way of a switch or radio areanetwork. Authentication can refer to determinations regarding whetherthe user requesting a service from the telecom network is authorized todo so within this network or not. Call control and switching can referdeterminations related to the future course of a call stream acrosscarrier equipment based on the call signal processing. Charging can berelated to the collation and processing of charging data generated byvarious network nodes. Two common types of charging mechanisms found inpresent day networks can be prepaid charging and postpaid charging.Service invocation can occur based on some explicit action (e.g. calltransfer) or implicitly (e.g., call waiting). It is to be noted thatservice “execution” may or may not be a core network functionality asthird party network/nodes may take part in actual service execution. Agateway can be present in the core network to access other networks.Gateway functionality can be dependent on the type of the interface withanother network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks include Geocasttechnology; broadcast technologies (e.g., sub-Hz, ELF, VLF, LF, MF, HF,VHF, UHF, SHF, THz broadcasts, etc.); Ethernet; X.25; powerline-typenetworking (e.g., PowerLine AV Ethernet, etc.); femto-cell technology;Wi-Fi; Zigbee, other 802.XX wireless technologies, WorldwideInteroperability for Microwave Access (WiMAX); Enhanced General PacketRadio Service (Enhanced GPRS); Third Generation Partnership Project(3GPP or 3G) Long Term Evolution (LTE); 3GPP Universal MobileTelecommunications System (UMTS) or 3GPP UMTS; Third GenerationPartnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB); High SpeedPacket Access (HSPA); High Speed Downlink Packet Access (HSDPA); HighSpeed Uplink Packet Access (HSUPA); GSM Enhanced Data Rates for GSMEvolution (EDGE) Radio Access Network (RAN) or GERAN; UMTS TerrestrialRadio Access Network (UTRAN); or LTE Advanced.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methodologieshere. One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: receivingsignal strength indicator information associated with a user equipment;receiving historical signal strength indicator information comprisinginformation relating to a historically available local wireless resourcedevice; and determining a probability that a wireless resource isavailable, wherein the probability is based on the signal strengthindicator information, independent of a location determination, theinformation relating to the historically available local wirelessresource device, independent of the location determination, and thehistorical signal strength indicator information, independent of thelocation determination.
 2. The system of claim 1, wherein the signalstrength indicator information comprises signal strength indicatorvalues associated with a received radio frequency signal.
 3. The systemof claim 1, wherein the historical signal strength indicator informationcomprises at least one set of historical signal strength indicators, andwherein the at least one set of historical signal strength indicatorscomprises historical signal strength indicator values associated withhistorically received radio frequency signals.
 4. The system of claim 3,wherein the determining the probability comprises determining a level ofsimilarity between the signal strength indicator information and the atleast one set of historical signal strength indicator information. 5.The system of claim 1, wherein the determining the probability comprisesdetermining a level of similarity between the signal strength indicatorinformation and the historical signal strength indicator information. 6.The system of claim 1, wherein the probability is a probability that ahistorically available local wireless resource device is presentlyavailable and is determined, at least in part, by comparing the signalstrength indicator information and the historical signal strengthindicator information without determining a location and in the absenceof an association of the location with the historically available localwireless resource device.
 7. The system of claim 1, wherein thedetermining further comprises determining that a condition relating toanalysis of the signal strength indicator information and the historicalsignal strength indicator information is satisfied.
 8. The system ofclaim 7, wherein the determining further comprises receiving a ruleassociated with the signal strength indicator information and thehistorical signal strength indicator information.
 9. The system of claim1, further comprising, forming an inference relating to the probabilitythat a historically available local wireless resource device ispresently available.
 10. The system of claim 9, wherein the inferringcomprises a trained classifier.
 11. The system of claim 1, wherein anenabled-disabled state, for a wireless radio device associated with theuser equipment, is determined based on the probability.
 12. A method,comprising: receiving, by a system comprising a processor, signalstrength indicator information associated with a user equipment;receiving, by the system, historical signal strength indicatorinformation comprising correlation information for a historical signalstrength indicator value and a historical availability of a localwireless resource device, wherein the correlation information is notbased on location information related to the local wireless resourcedevice; and determining, by the system, a probability that a wirelessresource is available, wherein the probability is based on the signalstrength indicator information and the historical signal strengthindicator information.
 13. The method of claim 12, wherein thedetermining the probability includes determining the probability basedon a level of similarity between the signal strength indicatorinformation and the historical signal strength indicator information.14. The method of claim 12, wherein the receiving historical signalstrength indicator information includes receiving other informationrelating to a historically available local wireless resource.
 15. Themethod of claim 14, wherein the determining the probability includesdetermining a probability that the historically available local wirelessresource is presently available.
 16. The method of claim 12, wherein thedetermining the probability includes forming an inference relating tothe historical signal strength indicator information.
 17. The method ofclaim 16, further comprising designating, by the system, anenabled-disabled state, for a wireless radio device associated with theuser equipment, based on the probability.
 18. A computer-readablestorage device storing executable instructions that, in response toexecution, cause a system comprising a processor to perform operations,comprising: receiving signal strength indicator information associatedwith a user equipment; receiving historical signal strength indicatorinformation comprising information relating to a correlation, not basedon location information, between a historical availability of a localwireless resource device and a historical signal strength indicatorvalue; and determining a probability based on the signal strengthindicator information and the historical signal strength indicatorinformation, wherein the probability is related to a presentavailability of a historically available local wireless resource device.19. The computer-readable storage device of claim 18, wherein theoperations further comprise receiving, based on the probability, acontrol value related to a state transition for a wireless radioassociated with the user equipment.
 20. The computer-readable storagedevice of claim 19, wherein the control value is related to anenabled-disabled state for the wireless radio.